Learning and generalization of motor skills by learning from demonstration

Is imitation learning the route to humanoid robots?

Schaal, S. (1999) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(99)01327-3 · Cited by 1,363

Movement imitation with nonlinear dynamical systems in humanoid robots

Ijspeert, A. J., Nakanishi, J., & Stefan Schaal, A. (2003) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2002.1014739 · Cited by 862

Robot learning from demonstration

Billard, A., & Siegwart, R. (2004) · Robotics Auton. Syst. · doi:10.1016/j.robot.2004.03.001 · Cited by 833

Computational approaches to motor learning by imitation

Schaal, S., Ijspeert, A. J., & Billard, A. (2003) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2002.1258 · Cited by 593

Imitation in Animals and Artifacts

Dautenhahn, K., & Nehaniv, C. (2002) · The MIT Press eBooks · doi:10.7551/mitpress/3676.001.0001 · Cited by 586

Learning Attractor Landscapes for Learning Motor Primitives

Ijspeert, A. J., Nakanishi, J., Stefan Schaal, A., Becker, S., Thrun, S., & Obermayer, K. (2002) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · Cited by 572

Behavioral dynamics of steering, obstable avoidance, and route selection.

Fajen, B. R., & Warren, W. H. (2003) · Journal of Experimental Psychology Human Perception & Performance · doi:10.1037/0096-1523.29.2.343 · Cited by 453

Operational Space Control: A Theoretical and Empirical Comparison

Nakanishi, J., Cory, R., Mistry, M., Peters, J., & Stefan Schaal, A. (2008) · The International Journal of Robotics Research · doi:10.1177/0278364908091463 · Cited by 381

Biologically-inspired dynamical systems for movement generation: Automatic real-time goal adaptation and obstacle avoidance

Hoffmann, H., Pástor, P., Park, D., & Schaal, S. (2009) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2009.5152423 · Cited by 290

Closed-loop manipulator control using quaternion feedback

Yuan, J. S. (1988) · IEEE Journal on Robotics and Automation · doi:10.1109/56.809 · Cited by 269

A Framework for Dexterous Manipulation

Ku, L. Y., Rogers, J., Strawser, P., Badger, J. M., Learned-Miller, E., & Grupen, R. A. (2018) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2018.8594497 · Cited by 1

LfD Training of Heterogeneous Formation Behaviors.

Squires, W. G., & Luke, S. (2018) · National Conference on Artificial Intelligence · Cited by 1

Motor Program Learning for Humanoid Robot Drawing

Makkar, D., Atoofi, P., Hamker, F. H., & Nassour, J. (2018) · IEEE-RAS International Conference on Humanoid Robots (Humanoids) · doi:10.1109/humanoids.2018.8624958 · Cited by 1

Long Time Sequential Task Learning From Unstructured Demonstrations

Zhang, H., Liu, Y., & Zhou, W. (2019) · IEEE Access · doi:10.1109/access.2019.2929107 · Cited by 1

An assistance approach for a powered knee exoskeleton during level walking and the effects on metabolic cost

Jang, J., Lim, B., & Shim, Y. (2019) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2019.8856852 · Cited by 1

Semi-supervised Learning From Demonstration Through Program Synthesis: An Inspection Robot Case Study

Smith, S. C., & Ramamoorthy, S. (2020) · Electronic Proceedings in Theoretical Computer Science · doi:10.4204/eptcs.319.7 · Cited by 1

A DMPs-based Switching Motion Planning Method for Robots with Obstacles

Wu, H., Zhai, D., Xia, Z., & Xia, Y. (2020) · Chinese Control Conference (CCC) · doi:10.23919/ccc50068.2020.9188839 · Cited by 1

Adaptive Obstacle Avoidance Optimization Algorithm Based on Learning from Demonstration

Li, W., Cheng, H., Liang, Z., Xiao, J., & Zhang, X. (2021) · IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems · doi:10.1109/cyber53097.2021.9588300 · Cited by 1

Control of a lower limb exoskeleton using Learning from Demonstration and an iterative Linear Quadratic Regulator Controller: A simulation study

Goldfarb, N., Zhou, H., Bales, C., & Fischer, G. S. (2021) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc46164.2021.9630810 · Cited by 1

Wireless Remote Control of Robot Dual Arms and Hands Using Inertial Measurement Units for Learning from Demonstration

Go, W., & Kim, J.-H. (2015) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-16841-8_69 · Cited by 1

Dynamically stable movement generation of a humanoid robot from demonstration: kicking a ball

Hyun, C., & Jo, S. (2010) · International Conference on Ubiquitous Robots and Ambient Intelligence · Cited by 1

Probability Redistribution using Time Hopping for Reinforcement Learning

Kormushev, P. S., Dong, F., & Hirota, K. (2009) · Spiral (Imperial College London) · Cited by 1

Markerless Visual Robot Programming by DemonstrationPreprint

Memmesheimer, R., Mykhalchyshyna, I., Seib, V., Theisen, N., & Paulus, D. W. R. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.11541 · Cited by 1

Hybrid system identification using switching density networksPreprint

Burke, M., Hristov, Y., & Ramamoorthy, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.04360 · Cited by 1

Bootstrapping Motor Skill Learning with Motion PlanningPreprint

Abbatematteo, B., Rosen, E., Tellex, S., & Konidaris, G. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.04736 · Cited by 1

Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: Present, Challenges, and Future ProspectsPreprint

Kadi, H. A., & Terzić, K. (2022) · Preprints.org · doi:10.20944/preprints202212.0305.v1 · Cited by 1

Phase-Free Dynamic Movement Primitives Applied to Kinesthetic Guidance In Robotic Co-Manipulation TasksPreprint

Braglia, G., Tebaldi, D., & Biagiotti, L. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4882367 · Cited by 1

From speech recognition to instruction learning

Vincke, J. (2015) · OPUS Publication Server of the University of Stuttgart (University of Stuttgart) · doi:10.18419/opus-3563

Interactive Task-Plan Learning

Dong, S. (2010) · National Conference on Artificial Intelligence

Research on the control strategy of robot imitation learning based on KL divergence

Yu, J., Liu, T., Ruan, X., Xu, C., & Men, Y. (2015) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc.2015.7161922

Intelligent Control: Its Identity and Some Noticeable Techniques

Bien, Z., & Suh, I. H. (2014) · Journal of Institute of Control Robotics and Systems · doi:10.5302/j.icros.2014.14.9012

Imitation-based motion programming for robotic manipulators

Fratu, A., & Fratu, M. (2014) · International Conference on Optimization of Electrical and Electronic Equipment · doi:10.1109/optim.2014.6850987

Transferring skills to robots for tasks with cyclic motions via dynamical systems approach

Vakanski, A., Janabi‐Sharifi, F., & Mantegh, I. (2012) · International Symposium on Optomechatronic Technologies · doi:10.1109/isot.2012.6403253

Contact state based representation of object relations in the environment for dexterous manipulations

Petsch, S., & Burschka, D. (2013) · IEEE/ASME International Conference on Advanced Intelligent Mechatronics · doi:10.1109/aim.2013.6584233

Developing Learning from Demonstration Techniques for Individuals with Physical Disabilities

Curran, W. (2015) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1145/2701973.2702710

HELP--Human assisted Efficient Learning Protocols

Subramanian, K. (2010) · Rutgers University Community Repository (Rutgers University) · doi:10.7282/t39p31dq

Motion-Based Learning

Suh, I., & Lee, S. H. (2016) · Context Aware Human-Robot and Human-Agent Interaction · doi:10.1007/978-3-319-19947-4_7

Learning Spatio-Temporal Feature Templates from Demonstrations for Optimization Based Trajectory Generation

Yin, X., & Chen, Q. (2015) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2015.160

Toward a Case-Based Framework for Imitation Learning in Robotic Agents.

Fitzgerald, T. (2015)

Learning manipulation affordances via intrinsically motivated exploration

Border, B. J. (2017) · Figshare · doi:10.4225/03/58b77bd38a928

Digital Representation and Modeling of Human Skill

Avizzano, C. A. (2012) · Human factors and ergonomics · doi:10.1201/b12704-13

Abstraction for Analogical Reasoning in Robotic Agents.

Fitzgerald, T., Thomaz, A. L., & Goel, A. K. (2016) · ICCBR Workshops

Dynamic movement primitives and force feedback: Teleoperation in precision grinding process

Açıkgöz, K., Parvizi, P., Donder, A., Ugurlu, M. C., & Konukseven, E. İ. (2017) · OpenMETU (Middle East Technical University)

Learning robotic manipulation from user demonstrations

Rahmatizadeh, R. (2017) · Journal of International Crisis and Risk Communication Research

Generalización de las trayectorias de un brazo robótico utilizando primitivas de movimiento dinámico y regresión de procesos gaussianos.

Solórzano, C. A. P., Gutiérrez, J. G. H., & Prieto, F. (2018) · Revista EIA · doi:10.24050/reia.v15i29.690

Methods for Improving Motion Planning Using Experience

Coleman, & Thornton, D. (2017)

Programming Everyday Task by Demonstration using Primitive Skills for a Manipulator

Hung, P. N., & Yoshimi, T. (2017) · IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems · doi:10.1109/cyber.2017.8446427

Integration of Robotic Perception, Action, and Memory

Ku, L. Y. (2021) · Scholarworks (University of Massachusetts Amherst) · doi:10.7275/12657464

Task Generalization for Robust One-Shot Robot Programming using Entity-based Resources

Orendt, E. M., & Henrich, D. (2018) · IEEE RO-MAN · doi:10.1109/roman.2018.8525633

Importance of effective teaching in robot motor skill learning

Cloud, J., Theofanidis, M., Brady, J., & Makedon, F. S. (2019) · International Conference on PErvasive Technologies Related to Assistive Environments (PETRA) · doi:10.1145/3316782.3322775

Migrating robot control systems, towards the universality of robotic brains

Neculoiu, P. (2012)

Discovery and recognition of motion primitives in human activitiesPreprint

Sanzari, M., Ntouskos, V., & Pirri, F. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1709.10494

Robotic Manipulation of Liquids

Yamaguchi, A., & Atkeson, C. G. (2019) · Journal of the Robotics Society of Japan · doi:10.7210/jrsj.37.477

Action Generation Adapted to Low-Level and High-Level Robot-Object Interaction States

Maestre, C., Mukhtar, G., Gonzales, C., & Doncieux, S. (2019) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2019.00056

Surfing on an uncertain edge: Precision cutting of soft tissue using torque-based medium classification

Straižys, A., Burke, M., & Ramamoorthy, S. (2020) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra40945.2020.9196623

Learning What To Say And What To Do: A Model For Grounding Language And Actions

Antunes, A. (2020) · PEARL (University of Plymouth) · doi:10.24382/952

Skill Transfer between Industrial Robots by Sparse learning

Lan, K. (2017)

Unsupervised Hierarchical Concept LearningPreprint

Roychowdhury, S., Sontakke, S., Sarkar, M., Puri, N., Aggarwal, M., Badjatiya, P., Krishnamurthy, B., & Itti, L. (2021) · arXiv (Cornell University)

Task-Adaptive Robot Learning from Demonstration under Replication with Gaussian Process Models.Preprint

Arduengo, M., Colomé, A., Sol, J. B., Sentis, L., & Carme Torras, A. (2020) · arXiv (Cornell University)

Skill Transfer between Humans and Robots Based on Dynamic Movement Primitives and Sparse Autoencoder

Li, M. (2017)

Movement Primitive Libraries Learning for Industrial Manipulation Tasks

Xue, A., Li, X., Liu, C., & Cao, X. (2021) · Communications in computer and information science · doi:10.1007/978-981-16-2336-3_26

Hierarchical Neural Dynamic PoliciesPreprint

Bahl, S., Gupta, A., & Pathak, D. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.05627

Learning Autonomous Mobility Using Real Demonstration Data

Gu, J., & Li, Z. (2021) · International Conference on Advanced Robotics (ICAR) · doi:10.1109/icar53236.2021.9659394

Video2Skill: Adapting Events in Demonstration Videos to Skills in an Environment using Cyclic MDP HomomorphismsPreprint

Sontakke, S., Roychowdhury, S., Sarkar, M., Puri, N., Krishnamurthy, B., & Itti, L. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.03813

Trajectory generalization based on spline curve and Gaussian distribution

Liyin, Z., & Liu, C. (2020) · International Conference on Information Science and Control Engineering · doi:10.1109/icisce50968.2020.00293

Enhancing Feature Selection in Single Shot Robot Learning by Using Multi-Modal Inputs

Groth, C. (2021) · International Conference on Artificial Intelligence for Industries (AI4I) · doi:10.1109/ai4i51902.2021.00010

Towards a User Adaptive Assistive Robot: Learning from Demonstration Using Navigation Functions

Papageorgiou, X. S., Dometios, A. C., & Tzafestas, C. S. (2021) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9636200

Learning to Optimize Control Policies and Evaluate Reproduction Performance from Human Demonstrations

Gesel, P., LaRoche, D. P., Arthanat, S., & Begum, M. (2021) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9636454

Learning State-Based Behavior Using Deep Neural Networks

Zalat, M. (2021) · doi:10.22215/etd/2021-14813

Admittance control: learning from humans through collaborating with humans

Wang, N., & Yang, C. (2022) · Elsevier eBooks · doi:10.1016/b978-0-32-390445-2.00013-1

Learning Algorithm Design for Human-Robot Skill Transfer

Li, C. (2019) · doi:10.23889/suthesis.52478

Intrinsic and extrinsic noise in motor skill acquisition

Zhang, Z. (2018) · Digital Repository Service (Northeastern University) · doi:10.17760/d20316412

Task Modeling

Vakanski, A., & Janabi‐Sharifi, F. (2017) · doi:10.1002/9781119091882.ch4

Abstraction in Data-Sparse Task Transfer (Extended Abstract)

Fitzgerald, T., Goel, A. K., & Thomaz, A. L. (2022) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2022/800

Emergence of tool construction and tool use through hierarchical reinforcement learning

Li, Q., & Choe, Y. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-96104-2.00008-7

Robotic Demonstration Learning Stability-Accuracy Trade-Off: From an Energy Function Learning Viewpoint

Jin, Z., Liu, A., Zhang, W., & Yu, L. J. (2023) · International Conference on Robotics, Intelligent Control and Artificial Intelligence (RICAI) · doi:10.1109/ricai60863.2023.10489246

Demonstration Quality-based Teleoperated Learning with Visual and Haptic Data in Bandwidth-Limited Environments

Prado, D. F., Ramachandrareddy, P., & Steinbach, E. G. (2024) · IEEE Mediterranean Electrotechnical Conference (MELECON) · doi:10.1109/melecon56669.2024.10608469

Verifiable Learned Behaviors via Motion Primitive Composition: Applications to Scooping of Granular Media

Benton, A., Solowjow, E., & Akella, P. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10611279

Leveraging Memory and Attention in a Kinematically Aware Robot: An Ideomotor-Inspired Approach to Implicit Command Understanding from IMU Sensor Data*

Guinot, L., Matsumoto, R., Takahashi, S., & Iwata, H. (2024) · IEEE International Conference on Advanced Intelligent Mechatronics (AIM) · doi:10.1109/aim55361.2024.10637214

Comparing Skill Transfer Between Full Demonstrations and Segmented Sub-Tasks for Neural Dynamic Motion Primitives

Hanks, G., Venture, G., & Hu, Y. H. (2024) · Machines · doi:10.3390/machines12120872

Language Conditioned Robotics: Current Trends and Future Directions – a Survey PaperPreprint

Ninov, N. N., & Asif, W. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.5073634

Learning and generating vehicle motion primitives from human-driving data

Li, G., Xue, J., Zhang, B., Zhao, K., & Tao, Z. (2025) · Robotica · doi:10.1017/s0263574724001681

A generic manipulation task instruction framework using interactive digital twin environment

Zhang, N., Zhao, Y., & Dai, S. (2025) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-025-15138-1

A Novel DMPs Based Approach to Comply ISO/TS 15066

Pupa, A., Vittorio, F. D., & Secchi, C. (2025) · Springer proceedings in advanced robotics · doi:10.1007/978-3-031-89471-8_8

Evaluation of the Human-Like Robotic Touch: A User Study

Tang, Y., Chand, T., Mamaev, I., Hein, B., & Croy, I. (2025) · International Conference on Rehabilitation Robotics · doi:10.1109/icorr66766.2025.11063003

KeyMPs: One-Shot Vision-Language Guided Motion Generation by Sequencing DMPs for Occlusion-Rich Tasks

Anarossi, E., Kwon, Y., Tahara, H., Tanaka, S., Shirai, K., Hamaya, M., Beltran-Hernandez, C. C., Hashimoto, A., & Matsubara, T. (2025) · IEEE Access · doi:10.1109/access.2025.3588975 · Cited by 1

IRS-guided policy search in model based reinforcement learning for virtually coupled train sets control

Zhu, L., Miao, J., Chen, C., Yan, B., Gong, T., Yu, F. R., & Tang, T. (2025) · Computers & Industrial Engineering · doi:10.1016/j.cie.2025.111372

Robot Learning Using Multi-Coordinate Elastic Maps

Hertel, B., & Azadeh, R. (2025) · International Conference on Ubiquitous Robots (UR) · doi:10.1109/ur65550.2025.11078120

RMAL-ODMPs: Orientation Skills Learning and Generalization Method for Robotic Manipulation

Wang, Z., Wang, Y., Chen, C., Peng, F., Hong, Y., Ai, Z., Tang, X., & Yan, R. (2025) · IEEE International Conference on Mechatronics and Automation (ICMA) · doi:10.1109/icma65362.2025.11120630

A Novel Dynamic Motion Primitives Framework for Safe Human-Robot Collaboration

Pupa, A., Vittorio, F. D., & Secchi, C. (2025) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra55743.2025.11127783

DMP with affordance templates: teleoperation-based learning from demonstration framework for contact-rich task

Lee, H., Kim, J., & Yang, G. (2025) · Intelligent Service Robotics · doi:10.1007/s11370-025-00639-5

FABRIC: FAbricating Bodily-Expressive Robots for Inclusive and Low-Cost Design

Arabi, A. A., & Kim, J. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11247443

Weight Regression for a Generalized Motion Primitive Formulation in Cooperative Hand Placement Tasks with Upper-Limb Prostheses

Cai, H., Greene, R., Hunt, C. L., & Thakor, N. V. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11246788

LAMPS: A Novel Robot Generalization Framework for Learning Adaptive Multi-Periodic Skills

Liu, Z., Luo, H., Liang, X., & Fang, Y. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11246906

Transferring Kinesthetic Demonstrations across Diverse Objects for Manipulation Planning

Das, D., Patankar, A., Chakraborty, N., Ramakrishnan, C. R., & Ramakrishnan, I. V. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11246024

Encoding Robot Behavior as Sensory-Based Adaptation of Learned Skillful Trajectories

Madera, J., Varveropoulos, L., & Fey, A. M. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11247078

Augmented-SVM for Gradient Observations with Application to Learning Multiple-Attractor Dynamics

Shukla, A., & Billard, A. (2014) · Springer eBooks · doi:10.1007/978-3-319-02300-7_1

Constraint-Informed Temporal Action Segmentation

Sliwowski, D., Park, S.-S., Lee, K.-H., Kim, D., Ryu, J.-H., & Lee, D. (2025) · International Conference on Control, Automation and Systems (ICCAS) · doi:10.23919/iccas66577.2025.11301312

iProDMP: An Enhanced Probabilistic Dynamic Movement Primitives Framework for Hip Exoskeleton-Assisted Lifting

Peng, S., Zhang, H., Liu, Z., He, W., & Cheng, L. (2026) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2026.3663234

Localized Obstacle Avoidance for Robots Based on Dynamic Movement Primitives With an External Force Model

Qiu, C., Wang, D., & Ju, Z. (2026) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2026.3671106

Regroupement de compétences robotiques en compétences plus générales

Matricon, A. (2018) · doi:10.70675/af76d8cdzdd2bz4c7dz9a07zbcb5b9ecd6b3

Discovering and exploiting the task hierarchy to learn sequences of motor policies for a strategic and interactive robot

Duminy, N. (2018) · doi:10.70675/84535353zb263z4942za26bz854b4e56a296

Autonomous and Online Generation of Skills Inferring Actions Adapted to Low-Level and High-Level Contextual States

Maestre, C. (2018) · theses.fr (ABES) · doi:10.70675/d5cf82abzf520z4560z9724zb53c5eb4a78b

Unsupervised pretraining of state representations in a rewardless environment

Merckling, A. (2021) · theses.fr (ABES) · doi:10.70675/e5589029zb8c4z4364zb923zbb4c70deaaf9

Learning Surgical Gestures from Data

Ginesi, M. (2026) · Springer tracts in advanced robotics · doi:10.1007/978-3-032-04247-7_5

Adaptive Dressing Assistance using a Dual Arm Robot for Individual Physique and Kyphosis

Kajiwara, T., Yamasaki, K., & Shibata, T. (2026) · Journal of the Robotics Society of Japan · doi:10.7210/jrsj.44.406

Learning a Unified Dynamic System Model Across Diverse Robotic Demonstration Tasks

Jin, Z., Si, W., Xu, R., Yang, C., & Lv, C. (2026) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2026.3718280

Learning Spatially Ambiguous Trajectories From Demonstrations via Phase-Modulated Dynamical Systems

Jung, H., Kim, J., & Kim, K. (2026) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2026.3732892

Interleaving Rhythmic and Discrete Control for the Manipulation of Underactuated Objects

Lokesh, R., & Dagmar, S. (2026) · IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob) · doi:10.1109/biorob66782.2026.11681853

Learning Algorithm Design for Human-Robot Skill Transfer

Li, C. (2019) · doi:10.23889/suthesis.54879

Learning tasks from a single demonstration

Atkeson, C. G., & Stefan Schaal, A. (2002) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.1997.614389 · Cited by 128

Programming-by-Demonstration of Robot Motions

Skoglund, A., Iliev, B., & Palm, R. (2010) · Advanced information and knowledge processing · doi:10.1007/978-1-84996-329-9_1

Learning and generalization of complex tasks from unstructured demonstrations

Niekum, S., Osentoski, S., Konidaris, G., & Barto, A. G. (2012) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2012.6386006 · Cited by 175

Programming-by-Demonstration of reaching motions—A next-state-planner approach

Skoglund, A., Iliev, B., & Palm, R. (2009) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2009.12.003 · Cited by 27

Adaptive Control for Human-Robot Skilltransfer: Trajectory Planning Based on Fluid Dynamics

Mayer, H. G., Nagy, I., Knoll, A., Braun, E. U., Lange, R., & Bauernschmitt, R. (2007) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2007.363583 · Cited by 40